code: def test_interaction_with_env(self):
      batch_size = 5
      reward_range = (-1, 1)
      ep = gym_env_problem.GymEnvProblem(
          base_env_name="KellyCoinflip-v0",
          batch_size=batch_size,
          reward_range=reward_range,
      )
  
      # Resets all environments.
      ep.reset()
  
      # Let's play a few steps.
      nsteps = 100
      num_trajectories_completed = 0
      num_timesteps_completed = 0
      # If batch_done_at_step[i] = j then it means that i^th env last got done at
      # step = j.
      batch_done_at_step = np.full(batch_size, -1)
      for i in range(nsteps):
          # Sample batch_size actions from the action space and stack them (since
          # that is the expected type).
          actions = np.stack(
              [ep.action_space.sample() for _ in range(batch_size)]
          )
  
          _, _, dones, _ = ep.step(actions)
  
          # Do the book-keeping on number of trajectories completed and expect that
          # it matches ep's completed number.
  
          num_done = sum(dones)
          num_trajectories_completed += num_done
  
          self.assertEqual(
              num_trajectories_completed,
              len(ep.trajectories.completed_trajectories),
          )
  
          # Get the indices where we are done ...
          done_indices = env_problem_utils.done_indices(dones)
  
          # ... and reset those.
          ep.reset(indices=done_indices)
  
          # If nothing got done, go on to the next step.
          if done_indices.size == 0:
              # i.e. this is an empty array.
              continue
  
          # See when these indices were last done and calculate how many time-steps
          # each one took to get done.
          num_timesteps_completed += sum(
              i + 1 - batch_done_at_step[done_indices]
          )
          batch_done_at_step[done_indices] = i
  
          # This should also match the number of time-steps completed given by ep.
          num_timesteps_completed_ep = sum(
              ct.num_time_steps
              for ct in ep.trajectories.completed_trajectories
          )
          self.assertEqual(
              num_timesteps_completed, num_timesteps_completed_ep
          )
  
      # Reset the trajectories.
      ep.trajectories.reset_batch_trajectories()
      self.assertEqual(0, len(ep.trajectories.completed_trajectories))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import gym
  from gym.spaces import Box
  from gym.spaces import Discrete
  import numpy
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.envs import env_problem
  from tensor2tensor.envs import env_problem_utils
  from tensor2tensor.envs import gym_env_problem
  from tensor2tensor.layers import modalities
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 6 for calling the GymEnvProblemTest obj's underlying member method code with example usage: obj.test_interaction_with_env() without return types